Product adoption is the key to success. That’s why companies invest heavily in customer experience initiatives that are specifically designed to engage customers and turn them into active users of their products and services. But how can you tell if those efforts are working? The answer lies in understanding customer conversations – both online and offline – with analytics-driven insights. By collecting and analyzing customer conversations, companies can gain valuable insights into individual customer experiences, track engagement metrics over time, identify areas of improvement in product adoption, and measure ROI on customer experience investments. In this blog post, we’ll take a closer look at how one financial services company used conversation analytics to pinpoint and resolve product issues that are hindering adoption.
A financial technology client launched a new version of their mobile application to improve usability and reduce the contact center burden. However, they noticed increased customer calls after the new roll-out.
As was a significant update, the initial assumption was that customers might need time to ask questions and familiarize themselves with the application before becoming more self-sufficient. The team kept an eye on the call volume based on the auto-generated conversation categories and noted the high login and connectivity issues.
Because the client had not seen the expected results with their new product enhancement, they used one of Level AI’s standard out-of-the-box reports to view the number of interactions by conversation category for an insight into whether the customers were having issues with the new application.
The data showed reconnection and relink as one of the highest customer issues reported, indicating that there may be some customer usability issues that the product team was not aware of.
To find out which sub-issue was causing this, the client queried for all conversational data within Level AI that fell under reconnection and relinking bank accounts, which showed the number of customers experiencing cache clearing and login errors was drastically higher. The new product updates were causing caching and login issues for many customers, and the client did not see the expected results because customers had difficulty logging in to interact with the new user experience.
The client immediately shared the insights and findings of this data analysis with their product team to investigate and isolate the bug causing the caching and login errors. The client also worked with call center managers to ensure that all of their agents have the necessary coaching and resources to adequately address the caching and login errors and the progress on the upcoming fix with their customers.
For more details about this use case and how four other companies use Level AI to uncover hidden insights with contact center analytics, download the e-book, From Insights to Action: Uncover Hidden Insights and Transform Contact Center Operations.
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